Mehul Motani received the B.E. degree from Cooper Union, New York, NY, the M.S. degree from Syracuse University, Syracuse, NY, and the Ph.D. degree from Cornell University, Ithaca, NY, all in Electrical and Computer Engineering.
Dr. Motani is currently an Associate Professor in the Electrical and Computer Engineering Department at the National University of Singapore (NUS) and a Visiting Research Collaborator at Princeton University. Previously, he was a Visiting Fellow at Princeton University. He was also a Research Scientist at the Institute for Infocomm Research in Singapore, for three years, and a Systems Engineer at Lockheed Martin in Syracuse, NY for over four years. His research interests include information and coding theory, machine learning, biomedical informatics, wireless and sensor networks, and the Internet-of-Things.
Dr. Motani was the recipient of the Intel Foundation Fellowship for his Ph.D. research, the NUS Annual Teaching Excellence Award, the NUS Faculty of Engineering Innovative Teaching Award, and the NUS Faculty of Engineering Teaching Honours List Award. He actively participates in the Institute of Electrical and Electronics Engineers (IEEE) and the Association for Computing Machinery (ACM). He is a Fellow of the IEEE and has served as the Secretary of the IEEE Information Theory Society Board of Governors. He has served as an Associate Editor for both the IEEE Transactions on Information Theory and the IEEE Transactions on Communications. He has also served on the Organizing and Technical Program Committees of numerous IEEE and ACM conferences.
Publication
▶ K.S. Fong and M. Motani, “POVE: A Preoptimized Vault of Expressions for Symbolic Regression Research and Benchmarking”, KDD 2025, Toronto, ON, CA, Aug 2025. [Link]
▶ S. Wongso, R. Ghosh and M. Motani, “Pointwise Information Measures as Confidence Estimators in Deep Neural Networks: A Comparative Study”, ICML 2025, Vancouver, BC, CA, Jul 2025. [Link]
▶ K.S. Fong and M. Motani, “Pareto-Optimal Fronts for Benchmarking Symbolic Regression Algorithms”, ICML 2025, Vancouver, BC, CA, Jul 2025. [Link]
▶ K.S. Fong and M. Motani, “FEAT-KD: Learning Concise Representations for Single and Multi-Target Regression via TabNet Knowledge Distillation”, ICML 2025, Vancouver, BC, CA, Jul 2025. [Link]
▶ J. Shi, K.S. Fong and M. Motani, “Analysis of Memory-Runtime Trade-offs in Caching Strategies for Genetic Programming Symbolic Regression”, GECCO 2025, Malaga, Spain, Jul 2025. [Link]
▶ K.S. Fong and M. Motani, “SLIME: Supralocal Interpretable Model-Agnostic Explanations via Evolved Equation-Based Surrogates”, GECCO 2025, Malaga, Spain, Jul 2025. [Link]
▶ Y. Leng, K.S. Fong and M. Motani, “Mutual Information-Based Evolutionary Feature Construction via Minimizing Redundancy and Maximizing Relevance”, GECCO 2025, Malaga, Spain, Jul 2025. [Link]
▶ K.S. Fong and M. Motani, “Discovering Shared Function Structures with Adaptable Parameters for Multi-Level Modeling via Symbolic Regression”, GECCO 2025, Hot Off the Press Track, Malaga, Spain, Jul 2025. [Link]
▶ L.W. Chia and M. Motani, “OCC Is Better Than Terahertz Wave for 6G”, MobiSys 2025 (Poster), Anaheim, CA, US, Jun 2025. [Link]
▶ Y. Feng and Z. Chen, M. Motani, H. Yang, M. Wang, T.Q.S. Quek, “Remote Online Estimation of the Wiener Process: a Preprocessing Method to Ensure Distortion Convergence”, Ann Arbor, MI, US, Jun 2025. [Link] [Link]
▶ R. Ghosh and M. Motani, “Ordered V-information Growth: A New Perspective on Shared Information”, International Conference on Artificial Intelligence and Statistics (AISTATS), Phuket, Thailand, May 2025. [Link]
▶ K.S. Fong and M. Motani, “SyREC: A Symbolic-Regression-Based Ensemble Combiner”, IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2024), Herndon, VA, USA, Oct 2024
▶ K.S. Fong and M. Motani, “MetaSR: A Meta-Learning Approach to Fitness Formulation for Frequency-Aware Symbolic Regression”, Genetic & Evolutionary Computation Conf. (GECCO), Melbourne, Australia, Jul 2024. [Link]
▶ K.S. Fong and M. Motani, “Enhancing Prediction, Explainability, Inference and Robustness of Decision Trees via Symbolic Regression-Discovered Splits”, GECCO 2024, Hot Off the Press Track, Melbourne, Australia, Jul 2024. [Link]
▶ S. Wongso, C.T. Leung, R. Ghosh, and M. Motani, “V-Fair Classifier: Analyzing Adversarially Fair Classifier from V-Information Perspective”, IEEE ISIT 2024 Workshop on Information-Theoretic Methods for Trustworthy Machine Learning, Athens, Greece, Jul 2024. [Link]
▶ K.S. Fong and M. Motani, “Explainable and Privacy-Preserving Machine Learning via Domain-Aware Symbolic Regression”, ACM Conference on Health, Inference, and Learning (ACM-CHIL 2024), New York, NY, Jun 2024. [Link] [Link]
▶ K.S. Fong and M. Motani, “Symbolic Regression for Discovery of Medical Equations: A Case Study on Glomerular Filtration Rate Estimation Equations”, IEEE Conference on Artificial Intelligence (IEEE CAI 2024), Singapore, Jun 2024. [Link] [Link]
▶ J.C.M. Tan and M. Motani, “Large Language Model (LLM) as a System of Multiple Expert Agents: An Approach to solve the Abstraction and Reasoning Corpus (ARC) Challenge”, IEEE Conference on Artificial Intelligence (IEEE CAI 2024), Singapore, Jun 2024. [Link] [Link]